Prediction the potential fishing grounds using machine learning and satellite data
2019
Arai, Y. (Remote Sensing Technology Center of Japan, Minato, Tokyo (Japan)) | Dehara, M.
Fishery prediction using satellite data has been reported by many studies. However, many of these studies use catches data sets from fishing boats and research vessels. In this study, using night-time visible images from satellite data instead of catches information, we predicted potential fishing grounds for saury using random forest, support vector machine, maximum entropy of machine learning. From September to December, the fishing ground predicted by machine learning showed moving from the north to the south as in the past catches reports. The predicted fishing ground distribution pattern was consistent with past reports. Some ships like as fleet of fishing boats were also located in the predicted fishing ground outside EEZ. The fishing ground zone predicted by using random forest showed the most reasonable in the three machine learning models. We suggest that it is possible to predict the potential fishing grounds only from satellite data sets.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Agriculture, Forestry and Fisheries Research Information Technology Center
Découvrez la collection de ce fournisseur de données dans AGRIS